Data science on industrial data -- Today's challenges in brown field applications

Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining the application of modern technologies especially in brown field applications. With this paper we want to show state of the art and what to expect when working with stock machines in the field. A major focus in this paper is on data collection which can be more cumbersome than most people might expect. Also data quality for machine learning applications is a challenge once leaving the laboratory. In this area one has to expect the lack of semantic description of the data as well as very little ground truth being available for training and verification of machine learning models. A last challenge is IT security and passing data through firewalls

System of Systems Lifecycle Management: A New Concept Based on Process Engineering Methodologies

In order to tackle interoperability issues of large-scale automation systems, SOA (Service-Oriented Architecture) principles, where information exchange is manifested by systems providing and consuming services, have already been introduced. However, the deployment, operation, and maintenance of an extensive SoS (System of Systems) mean enormous challenges for system integrators as well as network and service operators. The existing lifecycle management approaches do not cover all aspects of SoS management; therefore, an integrated solution is required. The purpose of this paper is to introduce a new lifecycle approach, namely the SoSLM (System of Systems Lifecycle Management). This paper first provides an in-depth description and comparison of the most relevant process engineering methodologies and ITSM (Information Technology Service Management) frameworks, and how they affect various lifecycle management strategies. The paper’s novelty strives to introduce an Industry 4.0-compatible PLM (Product Lifecycle Management) model and to extend it to cover SoS management-related issues on well-known process engineering methodologies. The presented methodologies are adapted to the PLM model, thus creating the recommended SoSLM model. This is supported by demonstrations of how the IIoT (Industrial Internet of Things) applications and services can be developed and handled. Accordingly, complete implementation and integration are presented based on the proposed SoSLM model, using the Arrowhead framework that is available for IIoT SoS. View Full-Tex

Industry 4.0: Industrial IoT Enhancement and WSN Performance Analysis

L'abstract è presente nell'allegato / the abstract is in the attachmen

The Role of Mixed Criticality Technology in Industry 4.0

[EN] Embedded systems used in critical systems, such as aeronautics, have undergone continuous evolution in recent years. In this evolution, many of the functionalities offered by these systems have been adapted through the introduction of network services that achieve high levels of interconnectivity. The high availability of access to communications networks has enabled the development of new applications that introduce control functions with higher levels of intelligence and adaptation. In these applications, it is necessary to manage different components of an application according to their levels of criticality. The concept of "Industry 4.0" has recently emerged to describe high levels of automation and flexibility in production. The digitization and extensive use of information technologies has become the key to industrial systems. Due to their growing importance and social impact, industrial systems have become part of the systems that are considered critical. This evolution of industrial systems forces the appearance of new technical requirements for software architectures that enable the consolidation of multiple applications in common hardware platforms-including those of different criticality levels. These enabling technologies, together with use of reference models and standardization facilitate the effective transition to this approach. This article analyses the structure of Industry 4.0 systems providing a comprehensive review of existing techniques. The levels and mechanisms of interaction between components are analyzed while considering the impact that the handling of multiple levels of criticality has on the architecture itself-and on the functionalities of the support middleware. Finally, this paper outcomes some of the challenges from a technological and research point of view that the authors identify as crucial for the successful development of these technologies.This research was funded by the Spanish Science and Innovation Ministry MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN201786520-C3-1-R.Simó Ten, JE.; Balbastre, P.; Blanes Noguera, F.; Poza-Lujan, J.; Guasque Ortega, A. (2021). The Role of Mixed Criticality Technology in Industry 4.0. Electronics. 10(3):1-16. https://doi.org/10.3390/electronics1003022611610

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Contributions for the exploitation of Semantic Technologies in Industry 4.0

120 p.En este trabajo de investigación se promueve la utilización de las tecnologías semánticas, en el entorno de la Industria 4.0, a través de tres contribuciones enfocadas en temas correspondientes a la fabricación inteligente: las descripciones enriquecidas de componentes, la visualización y el análisis de los datos, y la implementación de la Industria 4.0 en PyMEs.La primera contribución es una ontología llamada ExtruOnt, la cual contiene descripciones semánticas de un tipo de máquina de fabricación (la extrusora). En esta ontología se describen los componentes, sus conexiones espaciales, sus características, sus representaciones en tres dimensiones y, finalmente, los sensores utilizados para capturar los datos. La segunda contribución corresponde a un sistema de consulta visual en el cual se utiliza la ontología ExtruOnt y una representación en 2D de la extrusora para facilitar a los expertos de dominio la visualización y la extracción de conocimiento sobre el proceso de fabricación de una manera rápida y sencilla. La tercera contribución consiste en una metodología para la implementación de la Industria 4.0 en PyMEs, orientada al ciclo de vida del cliente y potenciada por el uso de tecnologías Semánticas y tecnologías de renderizado 3D.Las contribuciones han sido desarrolladas, aplicadas y validadas bajo un escenario de fabricación real

Reducing the acquisition cost of the next fighter jet using automation

The acquisition cost of fast-jets has increased exponentially since WWII, placing defence budgets under severe pressure. Fleet sizes are contracting as fewer new aircraft are ordered, and with new programmes few and far between the methods of assembling airframes have hardly changed in fifty-years. Modern airframes rely on traditional welded steel assembly fixtures and high accuracy machine tools, which represent a significant non-recurring cost that cannot be reconfigured for re-use on other programmes. This research investigates the use of automation to reduce the acquisition cost. Its aim is to demonstrate innovations, which will collectively assist in achieving the twin goals of Tempest, to be manufactured 50-percent faster and 50-percent cheaper, through the re-configuration and re-use of automation, creating a flexible factory-of-the-future. Two themes were explored, the UK-MOD’s acquisition process, to position this research in the timeframe of the next generation of fast-jet, and the use of automation in airframe assembly globally, specifically focusing on Measurement Assisted Assembly (MAA), part-to-part methods and predictive processes. A one-to-one scale demonstrator was designed, manufactured and assembled using MAA; and from the measurement data additively manufactured shims for the structure’s joints were produced. The key findings are that; metrology guided robots can position parts relative to one-another, to tolerances normally achieved using welded steel fixtures, maintaining their position for days, and can then be reconfigured to assemble another part of the structure. Drilling the parts during their manufacture on machine tools, using both conventional and angle-head tooling, enables them to be assembled, negating the requirement to use traditional craft-based skills to fit them. During the manufacture of the parts, interface data can be collected using various types of metrology, enabling them to be virtually assembled, creating a Digital Twin, from which any gaps between parts can be modelled and turned into a shim using an additive manufacturing process with the limitation that current AM machines do not produce layers thin enough to fully meet the shimming requirement. The acquisition process requires, a technology to be demonstrated at technology readiness level (TRL) 3 during the concept phase, and have a route-map to achieve TRL 6 in the development phase, following the assessment phase. The novel use of automation presented in this thesis has the potential to enable manufacturing assets to be re-configured and re-used, significantly reducing impacting the acquisition costs of future airframe programmes. Collectively the innovations presented can significantly reduce the estimated 75 percent of touch labour costs and 9 percent of non-recurring costs associated with assembling an airframe. These innovations will help to enable a digital transformation that, together with other Industry 4.0 technologies and methods, can collectively enable the automated manufacture of customised aerospace products in very-low volumes. This is of relevance not only to next generation fighter jets, but also to emerging sectors such as air-taxis